Название: MATLAB Medical Imaging Toolbox User's Guide (R2023b)Автор: MathWorks
Издательство: The MathWorks, Inc.
Год: September 2023
Страниц: 282
Язык: английский
Формат: pdf (true)
Размер: 12.3 MB
Visualize, register, segment, and label 2D and 3D medical images.
Medical Imaging Toolbox provides apps, functions, and workflows for designing and testing diagnostic imaging applications. You can perform 3D rendering and visualization, multimodal registration, and segmentation and labeling of radiology images. The toolbox also lets you train predefined Deep Learning networks (with Deep Learning Toolbox).
You can import, preprocess, and analyze radiology images from various imaging modalities, including projected X-ray imaging, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (US), and nuclear medicine (PET, SPECT). The Medical Image Labeler app lets you semi-automate 2D and 3D labeling for use in AI workflows. You can perform multimodal registration of medical images, including 2D images, 3D surfaces, and 3D volumes. The toolbox provides an integrated environment for end-to-end computer-aided diagnosis and medical image analysis.
You can install the Medical Imaging Toolbox Interface for Cellpose Library from Add-On Explorer. For more information about installing add-ons, see “Get and Manage Add-Ons”. The support package also requires Deep Learning Toolbox and Computer Vision Toolbox. Processing images on a GPU requires a supported GPU device and Parallel Computing Toolbox. The Cellpose Library uses Python. The Medical Imaging Toolbox Interface for Cellpose Library support package internally uses the pyenv function to interface with the Cellpose Library and automatically load a compatible Python environment. You do not need to configure the Python environment.
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